Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network

Chaogang Tang,Huaming Wu,Xianglin Wei,Joel J P C Rodrigues,Qing Li,Chunsheng Zhu

doi:10.1109/access.2020.2995797

Abstract

Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers-vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency).

Highlights

Intelligent transportation system (ITS) is a fully functioning ecosystem which makes the most of the Internet of Things, cloud computing, and mobile internet
We investigate the issue of resource allocation from the perspective of resource providers (i.e., R), with an aim to maximize its own benefits
The number of vehicular applications is explosively increasing with the development of ITS

Summary

INTRODUCTION

Intelligent transportation system (ITS) is a fully functioning ecosystem which makes the most of the Internet of Things, cloud computing, and mobile internet. C. Tang et al.: Intelligent Resource Allocation for Utility Optimization in RSU-Empowered Vehicular Network of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies, data and information can be shared and disseminated for better service provisioning. We investigate the resource allocation from the viewpoint of RSUs with a purpose of maximizing the utility values of them, while considering the real-time requirements of vehicular applications. Compared to task execution in cloud center, the response delay can be drastically reduced This merit makes it especially suitable for vehicular applications with strict delay requirements. An approximate but efficient resource allocation strategy is proposed that can intelligently respond to the resource requests in a real time fashion and optimize the utility value from the perspective of RSU-enabled cloudlet.

RELATED WORKS

2) OBJECTIVE FUNCTION

ALGORITHM DESIGN

CONCLUSION